# Modeling the Probabilistic Distribution of Unlabeled Data forOne-shot Medical Image Segmentation

arXiv: https://arxiv.org/abs/2102.02033 · HTML: https://mlsanity.com/paper/2102.02033/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 3 Feb 2021.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest](https://mlsanity.com/paper/1603.00275/) (2016)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [An Unsupervised Learning Model for Deformable Medical Image Registration](https://mlsanity.com/paper/1802.02604/) (2018)
- [Data augmentation using learned transformations for one-shot medical image segmentation](https://mlsanity.com/paper/1902.09383/) (2019)
- [Efficient Active Learning for Image Classification and Segmentation using a Sample Selection and Conditional Generative Adversarial Network](https://mlsanity.com/paper/1806.05473/) (2018)
- [DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation](https://mlsanity.com/paper/1904.08465/) (2019)
- [VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation](https://mlsanity.com/paper/1904.08963/) (2019)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Semi-Supervised and Task-Driven Data Augmentation](https://mlsanity.com/paper/1902.05396/) (2019)
- [CT-Realistic Lung Nodule Simulation from 3D Conditional Generative Adversarial Networks for Robust Lung Segmentation](https://mlsanity.com/paper/1806.04051/) (2018)
- [LT-Net: Label Transfer by Learning Reversible Voxel-wise Correspondence for One-shot Medical Image Segmentation](https://mlsanity.com/paper/2003.07072/) (2020)
- [Dilated FCN for Multi-Agent 2D/3D Medical Image Registration](https://mlsanity.com/paper/1712.01651/) (2017)
- [Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation](https://mlsanity.com/paper/1801.07198/) (2018)
- [Neural Multi-Atlas Label Fusion: Application to Cardiac MR Images](https://mlsanity.com/paper/1709.09641/) (2017)
- [NeurReg: Neural Registration and Its Application to Image Segmentation](https://mlsanity.com/paper/1910.01763/) (2019)

## Built on by (most cited first, 17 of 17)

- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [One-Prompt to Segment All Medical Images](https://mlsanity.com/paper/2305.10300/) (2023)
- [Geometric Visual Similarity Learning in 3D Medical Image Self-supervised Pre-training](https://mlsanity.com/paper/2303.00874/) (2023)
- [A Systematic Review of Few-Shot Learning in Medical Imaging](https://mlsanity.com/paper/2309.11433/) (2023)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [One-Shot Segmentation of Novel White Matter Tracts via Extensive Data Augmentation](https://mlsanity.com/paper/2303.06852/) (2023)
- [Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning](https://mlsanity.com/paper/2502.05282/) (2025)
- [One-shot Joint Extraction, Registration and Segmentation of Neuroimaging Data](https://mlsanity.com/paper/2307.15198/) (2023)
- [UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline](https://mlsanity.com/paper/2212.10390/) (2022)
- [Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2408.03616/) (2024)
- [Dual-Flow Transformation Network for Deformable Image Registration with Region Consistency Constraint](https://mlsanity.com/paper/2112.02249/) (2021)
- [AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation](https://mlsanity.com/paper/2309.00971/) (2023)
- [Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2410.09539/) (2024)
- [End-to-End Deep Learning for Structural Brain Imaging: A Unified Framework](https://mlsanity.com/paper/2502.18523/) (2025)
- [SAM-Guided Robust Representation Learning for One-Shot 3D Medical Image Segmentation](https://mlsanity.com/paper/2504.20501/) (2025)
- [Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection](https://mlsanity.com/paper/2605.01563/) (2026)
- [H2AL: Hyperbolic Hierarchy-aware Aggregative Learning for Registration-based Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2608.07340/) (2026)

## Abstract (arXiv)

Existing image segmentation networks mainly leverage large-scale labeled datasets to attain high accuracy. However, labeling medical images is very expensive since it requires sophisticated expert knowledge. Thus, it is more desirable to employ only a few labeled data in pursuing high segmentation performance. In this paper, we develop a data augmentation method for one-shot brain magnetic resonance imaging (MRI) image segmentation which exploits only one labeled MRI image (named atlas) and a few unlabeled images. In particular, we propose to learn the probability distributions of deformations (including shapes and intensities) of different unlabeled MRI images with respect to the atlas via 3D variational autoencoders (VAEs). In this manner, our method is able to exploit the learned distributions of image deformations to generate new authentic brain MRI images, and the number of generated samples will be sufficient to train a deep segmentation network. Furthermore, we introduce a new standard segmentation benchmark to evaluate the generalization performance of a segmentation network through a cross-dataset setting (collected from different sources). Extensive experiments demonstrate that our method outperforms the state-of-the-art one-shot medical segmentation methods. Our code has been released at https://github.com/dyh127/Modeling-the-Probabilistic-Distribution-of-Unlabeled-Data.